Overcoming the energy versus delay trade-off in cloud network reconfiguration
Bibliographic record
Abstract
Cloud computing calls for efficient solutions to manage the energy consumption of the transport, process and storage services. Recently, we have shown that energy savings in the cloud network and the data centers are at the expense of increased delay; hence degraded service quality. In this paper we propose a new scheme, Delay and Power Minimized Provisioning (DePoMiP) to address energy versus delay tradeoff in the cloud network. DePoMiP reconfigures the cloud network and provisions the demands by jointly minimizing the energy consumption and propagation delay. Through simulations, we compare DePoMiP to our previously proposed heuristics for delay-minimized provisioning and power-minimized provisioning of the demands. Simulation results show that DePoMiP mimics power-minimized provisioning in terms of power consumption while it provisions the demands with a few microseconds higher propagation delay when compared to delay-minimized provisioning. Furthermore, its low channel utilization in the IP over WDM transport network, as well as its fairness among the nodes in terms of power consumption, makes DePoMiP a promising solution for the problem of energy-efficient reconfiguration of the cloud network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".